Logging-based identification and evaluation of karst fractures in the eastern Right Bank of the Amu Darya River, Turkmenistan

Zhang, Shudong; Ren, Xingguo; Luo, Li; Guo, Tingliang; Liang, Xusheng

doi:10.1016/j.ngib.2019.01.008

Cited by 4 publications

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“…In addition, due to a lack of data and a poor understanding of the characteristics of the Callovian-Oxfordian carbonate reservoir in the whole block, the exploration and development are not productive. In recent years, with increasingly rich data in this area, many scholars have conducted in-depth research on the characteristics and evolution laws of its sedimentary facies and reservoirs [7][8][9][10][11][12][13], but most research was still theoretical. Therefore, it is difficult to efficiently guide oil and gas exploration and development in the whole area, even the whole basin, by exploring the types and distribution laws of reservoirs in the relatively small-scale structural zone.…”

Section: Introductionmentioning

confidence: 99%

Types and Distribution Characteristics of Callovian-Oxfordian Reservoir on the Right Bank of Amu Darya River in Turkmenistan

Wang¹

2023

Geofluids

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Given the characteristics of significant or enormous resources, a wide range, many reservoir types, and challenging exploration on the right bank of the Amu Darya River, we systematically studied the characteristics of its reservoirs. Based on the core description, thin sections in combination with logging and seismic facies characteristics as well as regional tectonic-sedimentary background, the Callovian-Oxfordian platform and the gentle slope sedimentary system in the Amu Darya Basin of Turkmenistan were summarized. Sedimentary subfacies such as evaporation platform, restricted platform, open platform, and platform margin reef are developed in block A and its west. In contrast, the upper slope, lower slope, and basin sedimentary subfacies are developed in the east of block A. On this basis, the main reservoir types on the right bank of the Amu Darya River are summarized, namely, porous reservoir, vuggy reservoir, fractured-porous reservoir, and fractured-vuggy reservoir. After describing the characteristics of various reservoirs in detail, the main controlling factors, development patterns, and distribution rules of the development of different reservoirs are summarized. Specifically, based on the platform reef, the porous reservoir and vuggy reservoirs are developed mainly on the concealed palaeouplift in the study area and are greatly influenced by atmospheric freshwater leaching and buried dissolution. Based on the dominant sedimentary facies, the fractured-porous reservoirs are mainly developed in the central areas on the right bank of the Amu Darya River. In the later period, hydrothermal fluid and hydrocarbon-generating acidic fluid can dissolve the reservoir through strike-slip faults and their associated fractures. Diagenetic fluid enters the reef through faults and associated fractures to form dissolved reservoir bodies of a certain scale. The fractured-vuggy reservoirs are mainly controlled by faults and dissolution and are mainly developed near the eastern thrust fault on the right bank of the Amu Darya River, effectively guiding the direction for further exploration and development in this area.

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Section: Introductionmentioning

confidence: 99%

Types and Distribution Characteristics of Callovian-Oxfordian Reservoir on the Right Bank of Amu Darya River in Turkmenistan

Wang¹

2023

Geofluids

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Practice and Understanding of Efficient Development of Overseas Complex Intra-Platform Uplift Gasfield: A Case Study of S Gasfield

Liu,

Wen,

Zheng

et al. 2024

Springer Series in Geomechanics and Geoengineering

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Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow

2021

J. Phys.: Conf. Ser.

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Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.

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Logging-based identification and evaluation of karst fractures in the eastern Right Bank of the Amu Darya River, Turkmenistan

Cited by 4 publications

References 0 publications

Types and Distribution Characteristics of Callovian-Oxfordian Reservoir on the Right Bank of Amu Darya River in Turkmenistan

Types and Distribution Characteristics of Callovian-Oxfordian Reservoir on the Right Bank of Amu Darya River in Turkmenistan

Practice and Understanding of Efficient Development of Overseas Complex Intra-Platform Uplift Gasfield: A Case Study of S Gasfield

Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow

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